Code and data from: Perceived and observed biases within scientific communities: a case study in movement ecology

Creators
  • Allison K Shaw
  • Leila Fouda
  • Stefano Mezzini
  • Dongmin Kim
  • Nilanjan Chatterjee
  • David Wolfson
  • Briana Abrahms
  • Nina Attias
  • Christine Beardsworth
  • Roxanne Beltran
  • Sandra Binning
  • Kayla Blincow
  • Ying-Chi Chan
  • Emanuel A. Fronhofer
  • Arne Hegemann
  • Edward Hurme
  • Fabiola Iannarilli
  • Julie Kellner
  • Karen D McCoy
  • Kasim Rafiq
  • Marjo Saastamoinen
  • Ana Sequeira
  • Mitchell Serota
  • Petra Sumasgutner
  • Yun Tao
  • Martha Torstenson
  • Scott Yanco
  • Kristina Beck
  • Michael Bertram
  • Larissa Teresa Beumer
  • Maja Bradarić ORCID logo
  • Jeanne Clermont
  • Diego Ellis Soto
  • Monika Faltusová
  • John Fieberg
  • Richard Hall
  • Andrea Kölzsch
  • Sandra Lai
  • Larisa Lee-Cruz
  • Matthias-Claudio Loretto
  • Alexandra Loveridge
  • Marcus Michelangeli
  • Thomas Mueller
  • Louise Riotte-Lambert
  • Nir Sapir
  • Martina Scacco
  • Claire S. Teitelbaum
  • Francesca Cagnacci
Publication date 21-05-2025
Description
Who conducts biological research, where, and how results are disseminated varies among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical step towards addressing them. We documented perceived and observed biases in movement ecology, a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. We surveyed attendees before an international conference to assess a baseline within-discipline perceived bias (uninformed perceived bias). We analysed geographic patterns in Movement Ecology articles, finding discrepancies between the country of the authors’ affiliation and study site location, related to national economics. We analysed race-gender identities of USA biology researchers (the closest-to-our-sub-discipline with data available), finding that they differed from national demographics. Finally, we discussed the quantitatively-observed bias at the conference, to assess within-discipline perceived bias informed with observational data (informed perceived bias). Although the survey indicated most conference participants as bias-aware, conversations only covered a subset of biases. We discuss potential causes of bias (parachute-science, fieldwork accessibility), solutions, and the need to evaluate mitigatory action effectiveness. Undertaking data-driven analysis of bias within sub-disciplines can help identify specific barriers and move towards the inclusion of a greater diversity of participants in the scientific process.
Publisher Zenodo
Organisations
  • Faculty of Science (FNWI) - Institute for Biodiversity and Ecosystem Dynamics (IBED)
Document type Dataset
DOI https://doi.org/10.5281/zenodo.15481348
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